Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99138
Title: Learning image similarity from Flickr groups using fast kernel machines
Authors: Wang, Gang
Hoiem, Derek
Forsyth, David
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Wang, G., Hoiem, D., & Forsyth, D. (2012). Learning Image Similarity from Flickr Groups Using Fast Kernel Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2177-2188.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: Measuring image similarity is a central topic in computer vision. In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. This method can produce a kernel classifier that is more accurate than a linear classifier on tens of thousands of examples in minutes.
URI: https://hdl.handle.net/10356/99138
http://hdl.handle.net/10220/13494
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2012.29
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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